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It uses trained artificial neural networks to pre-characterize red cells by size, color, shape and inclusions into 21 morphological characteristics. Operators confirm or change each suggestion. Method comparison studies ran at five clinical sites, two in the United States and three outside it. The main comparison with manual microscopy covered 1181 samples. FDA reviewers report that the results met the predefined acceptance criteria. For schistocytes, target cells, sickle cells, tear drop cells and parasites, efficiency was 90.6%, sensitivity 85% and specificity 93.6%. Agreement on cell size with an automated counter was lower, at 81.3%. FDA cleared the module on 1 August 2017. The results include operator review, so the accuracy of the networks alone is not reported.","replication":{"produced_by":["Venous blood was collected in K3EDTA tubes, and one slide was prepared from each sample.","At one site an expert operator confirmed or overrode the suggested grading, and at the others operators re-characterized individual cell images."],"must_hold":["Skilled operators trained on the device and in recognizing blood cells review the suggestions.","The blood samples have been flagged as abnormal by an automated cell counter."],"echoes":[]}}},"provenance":{"kind":"legacy_record","basis":"Editorial assignment.","record_fields":["account","replication"],"source_refs":[]},"review_state":"record_mapped","coverage":"partial"}},"summary":{"total":91,"addressed":91,"known":27,"reviewed":2,"reviewed_complete":2,"reviewed_partial":0,"record_mapped":25,"review_not_assessed":64,"not_assessed":0,"complete":false,"by_state":{"value_present":27,"not_reported":0,"unresolved":64,"not_assessed":0,"not_applicable":0,"withheld":0,"source_unavailable":0}}},"presentation_version":"aa09f1c799addd7d02e61841fee14a1068cb507a891f13d163d1314f07b8d904"}